Enhancing tumor-specific recognition of programmable synthetic bacterial consortium for precision therapy of colorectal cancer

Probiotics hold promise as a potential therapy for colorectal cancer (CRC), but encounter obstacles related to tumor specificity, drug penetration, and dosage adjustability. In this study, genetic circuits based on the E. coli Nissle 1917 (EcN) chassis were developed to sense indicators of tumor microenvironment and control the expression of therapeutic payloads. Integration of XOR gate amplify gene switch into EcN biosensors resulted in a 1.8-2.3-fold increase in signal output, as confirmed by mathematical model fitting. Co-culturing programmable EcNs with CRC cells demonstrated a significant reduction in cellular viability ranging from 30% to 50%. This approach was further validated in a mouse subcutaneous tumor model, revealing 47%-52% inhibition of tumor growth upon administration of therapeutic strains. Additionally, in a mouse tumorigenesis model induced by AOM and DSS, the use of synthetic bacterial consortium (SynCon) equipped with multiple sensing modules led to approximately 1.2-fold increased colon length and 2.4-fold decreased polyp count. Gut microbiota analysis suggested that SynCon maintained the abundance of butyrate-producing bacteria Lactobacillaceae NK4A136, whereas reducing the level of gut inflammation-related bacteria Bacteroides. Taken together, engineered EcNs confer the advantage of specific recognition of CRC, while SynCon serves to augment the synergistic effect of this approach.


II. Supplemental Figures
Supplemental Figure 1 to 36

III. Supplemental Tables
Supplemental Table 1 to 6

Molecular biology
The encoded information of all encoded genes or functional DNA fragments were obtained from NCBI (https://www.ncbi.nlm.nih.gov/),iGEM Standard Biology Parts (https://parts.igem.org/)and BioCyc databases (https://biocyc.org/).In particular, the related DNA sequences of amplifying genetic logic gates were reported and annotated in previous study 1.The DNA sequences were amplified from E. coli DH5α genomic DNA, plasmid pGEN-luxCDABE (P8781, MiaoLing, China), or synthetized by Genewiz (China) and Twist (USA, 2022 iGEM sponsor).mRFP was stored and provided in lab.All recombinant plasmids were based on pSB1A3 or pSB4C5 backbones (Supplemental Figure 1).pSB1A3 carries ampicillin resistant gene and a high copy replicon ColE1, while pSB4C5 carries chloramphenicol resistant gene and a low copy replicon pSC101.Amplified primers were synthesized by GENERAL Biol (Anhui, China).All recombinant plasmids were constructed using one-step cloning kit (C113, Vazyme, China) or Blunt Kination ligation kit (6127A, Takara, Japan) (Supplemental Table 2).All constructed plasmids were first transformed into E. coli DH5α for preservation and amplification, verified by DNA sequencing (Tsingke, China), and then transformed into EcN.Full information of programmable strains can be obtained in Supplemental Table 3 and bacteria member of synthetic consortia is listed in Supplemental Table 4.

Modeling
To describe the behavior of biosensors, we developed an ordinary differential equation describing normalized mRFP fluorescence (mRFP/cell) () generation in response to ambient lactate concentration (, equation (1)), protons (H !, equation ( 2)), and oxygen level (O " , equation ( 3)) via XOR Switch, regulated by transcription factors LldR 2, CadC 3 and FNR 4. For simplicity, the timescale for binding and transcription reactions is assumed much faster than that for translation 5 , 6. Bacterial growth is modelled using the logistic equation with  #$% as the maximum population size,  & is the degradation rate and  ' is the growth rate (equation (4)) 7. The relationship between bacteria degradation rate ( & ) and lysis protein ( ( ) can be defined as hill equation (assumed hill coefficient is 1) (equation 5) 8. Since φX174E is placed downstream of pLldR, pCadC, and pPepT, the lysis proteins concentration ( ( )) is induced by ambient lactate (equation ( 6)), H + (equation ( 7)), and oxygen (equation ( 8)).According to equations ( 4) and ( 5), the time-dependent changes of the population of lactate (equation ( 9)), pH (equation ( 10)) and hypoxia (equation ( 11)) induced lysis biosensors were described.As mRFP production depends on both bacterial population and inducing signals, we can derive a formula to describe the expression of mRFP () in lactate induced lysis biosensors (equation ( 12)).Additionally, we also presented equations to describe the mRFP production in pH (equation ( 13)) and hypoxia (equation ( 14)) induced lysis biosensors.Parameter fitting was based on the dataset collected in experiments.Initial values for parameters were set using reported values 8 , 9, and optimization of parameters was performed using the least squares analysis based on the Levenberg-Marquardt algorithm (least_squares function, Scipy.optimizelibrary).Specifically, a residual function, r = ymeasured data−yprediction, was defined to calculate the differences between model predictions and actual measurement data given the model parameters.Iterative adjustments were made to minimize the sum of squared residuals (SSE=∑r 2 ) to ensure convergence of the fitting process.

Absolute quantitative analysis
Briefly, stool DNA was extracted using the TIANamp fecal DNA Kit (Tiagen Biotechnology, China).Then, real-time quantitative PCR (qPCR) was performed on DNA using specific primers for total bacteria, EcN and recombinant strains.All qPCR primer sequences are listed in Supplemental Table 5.The target DNA sequences were cloned to pMD-18T (Takara) and the recombinant plasmid was employed to establish qPCR standard curve.qPCR was performed on QuantStudio 5 (Applied Biosystems, USA) using TB Green Premix Ex Taq II (RR820, Takara).The gene copy numbers were calculated.All measurements were repeated three times.

Quantitation of gene expression
Total RNA from the colon tissue was extracted from individual homogenates with the RNAprep Pure Tissue Kit (DP431, TIANGEN Biotech) and reverse-transcribed into cDNA using the HiScript 1st Strand cDNA Synthesis kit (R111, Vazyme Biotech, China,).Then, a total of 100 ng of cDNA was mixed with TB Green Premix Ex Taq II (RR802, Takara) and quantitative reverse transcription PCR (qRT-PCR) was performed on QuantStudio 5 (Applied Biosystems, ThermoFisher, USA).The primer sequences of the target genes are listed in Supplemental Table 6.GAPDH gene was used as internal controls and the mRNA expression level of each gene was calculated using 2 −ΔΔCt method.

Protein expression analysis
Through reverse PCR and BKL kit (Takara, Japan), 6 x His-Tag was introduced at the 3 'end of hlyE.The dual-vector systems containing sensing and therapeutic elements were constructed as described.In the subcutaneous tumor mouse model, tumor samples were obtained 12 hours after administering the engineered strains via intratumoral injection.The total proteins of tumor samples were extracted by homogenization with RIPA lysis buffer provided by Total protein extraction kit (W034-1-1, Nanjing Jiancheng Bioengineering Institute).After centrifugation at 12 000g for 15 min at 4 °C, the supernatants were collected, and the protein concentrations were determined by Bradford Protein Assay Kit (Beyotime).By 10% sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE), proteins were separated and transferred onto a PVDF membrane (Millipore, MA, USA).The membrane was then blocked with skimmed milk (5%; TBS-Tween) for 1 hours and incubated overnight at 4 °C with specific mouse primary antibody against His-Tag (66005-1, proteintech).